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  • Open Access

Bacterial DNA patterns identified using paired-end Illumina sequencing of 16S rRNA genes from whole blood samples of septic patients in the emergency room and intensive care unit

BMC Microbiology201818:79

https://doi.org/10.1186/s12866-018-1211-y

  • Received: 13 August 2017
  • Accepted: 27 June 2018
  • Published:

Abstract

Background

Sepsis refers to clinical presentations ranging from mild body dysfunction to multiple organ failure. These clinical symptoms result from a systemic inflammatory response to pathogenic or potentially pathogenic microorganisms present systemically in the bloodstream. Current clinical diagnostics rely on culture enrichment techniques to identify bloodstream infections. However, a positive result is obtained in a minority of cases thereby limiting our knowledge of sepsis microbiology. Previously, a method of saponin treatment of human whole blood combined with a comprehensive bacterial DNA extraction protocol was developed. The results indicated that viable bacteria could be recovered down to 10 CFU/ml using this method. Paired-end Illumina sequencing of the 16S rRNA gene also indicated that the bacterial DNA extraction method enabled recovery of bacterial DNA from spiked blood. This manuscript outlines the application of this method to whole blood samples collected from patients with the clinical presentation of sepsis.

Results

Blood samples from clinically septic patients were obtained with informed consent. Application of the paired-end Illumina 16S rRNA sequencing to saponin treated blood from intensive care unit (ICU) and emergency department (ED) patients indicated that bacterial DNA was present in whole blood. There were three clusters of bacterial DNA profiles which were distinguished based on the distribution of Streptococcus, Staphylococcus, and Gram-negative DNA. The profiles were examined alongside the patient’s clinical data and indicated molecular profiling patterns from blood samples had good concordance with the primary source of infection.

Conclusions

Overall this study identified common bacterial DNA profiles in the blood of septic patients which were often associated with the patients’ primary source of infection. These results indicated molecular bacterial DNA profiling could be further developed as a tool for clinical diagnostics for bloodstream infections.

Keywords

  • Sepsis
  • Illumina
  • 16 s rDNA sequencing
  • Molecular profiling
  • Bloodstream infections

Background

Sepsis refers to a systemic inflammatory response resulting from pathogenic microorganisms invading normally sterile tissues, fluids or body cavities [1]. It is often triggered by infections which have spread systemically as well as primary bloodstream infections [1]. Although any microbial agent can be implicated in sepsis, over 80% of bloodstream infections are attributed to bacteria [26]. The most commonly isolated bacteria from sepsis related bloodstream infections are Staphylococcus aureus, coagulase-negative Staphylococci (CoNS), Enterococcus species, Escherichia coli, and Pseudomonas aeruginosa [3].

Currently, sepsis bloodstream infections are primarily considered as a monomicrobial infection with rare cases of polymicrobial sepsis [7, 8]. However, these results are based on clinical diagnostic blood culture confirmed infections, which currently represents a minority of sepsis cases. We previously described a novel approach of extracting bacterial DNA from saponin-treated whole blood for use in 16S rRNA bacterial DNA analysis with Illumina sequencing [9]. Case study analysis revealed successful application of this novel approach to blood samples from septic patients in the intensive care unit (ICU). In this study, whole blood samples from expanded cohorts of ICU and emergency department (ED) patients presenting with clinical manifestations of sepsis were analyzed. The goal was to determine if molecular sequencing of bacterial DNA in the bloodstream correlated to clinical infection. Bacterial DNA profiles were analyzed alongside relevant blood culture and clinical data. This strengthened the interpretation of the DNA sequencing data as there was good concordance between the principal bacterial DNA recovered and other cultivation based data. This study supports the use of molecular profiling to augment blood culture diagnostics for identification of bacteria involved in bloodstream infections. In addition, the sensitivity of next-generation sequencing also allowed for detection of polymicrobial infections that are likely under-represented using culture-based enrichment methodology.

Methods

Study design

This work was conducted under the aegis of the Alberta Sepsis Network, a multi-year prospective cohort study designed to gather clinical, laboratory, and immunologic data on adult and pediatric patients admitted to the ED or the ICU with a provisional diagnosis of sepsis. Samples were collected from 2010 to 2014 at two hospitals in Calgary, Alberta, Canada. The date on which samples were collected was not provided to protect patient identity. Adult patient enrolment criteria included individuals 18 years or older admitted to the ICU of the Foothills Medical Center who met the published criteria for systemic inflammatory response syndrome (SIRS) and clinical suspicion or confirmation of infection within the first 24 h of admission or within the first 24 h of a newly acquired infection [10, 11]. SIRS criteria included; body temperature > 38 °C or < 36 °C, heart rate > 90/min, evidence of hyperventilation by respiratory rate > 20/min or PaCO2 < 32 mmHg, and white blood cell count > 12,000 cells/μl [11]. At the time of sample collection, the quick sequential organ failure assessment (qSOFA) criteria were not in clinical use [10]. The SOFA score was not regularly collected at the time of sampling, which was prior to the implementation of sepsis-3, but was available for the majority of patients admitted to the ICU [11]. As such, it was not used as an enrolment criterion. Exclusion criteria included patients in which life supportive care was deemed to be inappropriate. Adult ED patients were enrolled if they were over 18 years of age, and within the first 24 h of admission to the ED, two or more SIRS criteria and clinical suspicion or confirmation of infection. Pediatric ED patients were enrolled at the Alberta Children’s Hospital, Calgary, Alberta if they met the following criteria; under the age of 18, greater than two SIRS criteria present, clinical suspicion or confirmation of infection, and antibiotic treatment ordered for the suspected or confirmed infection and ongoing supportive care was deemed to be appropriate.

Blood was also collected from 12 healthy adults as the final control. These adults were chosen since they would represent the potential for contaminating DNA from the blood collection process including skin-associated bacteria or bacterial DNA present in the sterile vacutainers [12]. The results from these samples were previously reported [9].

Sample collection

Sample collection for this study was done as previously described [9] using agreed upon standard operating procedures. Trained and licensed nurses or phlebotomists collected whole blood and biological samples.

Whole blood samples used for analysis were obtained on Day 1 of ICU admission for sepsis or during presentation to ED with suspected sepsis. Based on the ASN guidelines for blood collection, a maximum of 4 ml of blood and 2 ml of blood were collected from adult and paediatric patients, respectively. Blood was collected from a fresh peripheral venous vascular injection into sterile K2EDTA spray coated vacutainers under aseptic techniques (BD Diagnostics, Mississauga, ON). For patients admitted to the ICU, samples were collected from central arterial or venous lines which were inserted within the first 12 h of ICU admission under aseptic technique [13].

Patient demographics, laboratory and clinical data

Clinical and diagnostic laboratory data was collected following enrolment. Data was considered relevant to the sample if collected within a 24-h period prior to or after enrolment in the study. Clinical data was obtained from the Alberta Sepsis Network database which included patient demographics, admitting diagnosis, APACHEII score [14] and the sepsis-related organ failure assessment (SOFA) score [15]. No ancestry data was collected as it is not part of patient charts in Canada. Each patient was identified only by a unique identifier based on the site in which the sample was obtained; FED samples represented adult ED patient samples, ASN samples represented adult ICU patient samples, ASNC represented adult healthy control samples, and AERG represented pediatric ED patient samples. Clinical laboratory results were collected from Calgary Laboratory Services and included all diagnostic cultures done that were relevant to the patient’s clinical presentation as well as all pharmacy related data for therapy administered.

Saponin treatment and DNA extraction from whole blood

Blood samples noted above were then processed with a custom saponin digestion prior to DNA extraction protocol. Methods for both steps were performed as outlined in Faria et al., (2015) [9]. Briefly, lysis of 1.5 mL of whole blood was achieved using 0.85% saponin (Sigma-Aldrich, USA). Lysed products were removed by centrifugation at 20,800 rcf for 15 min. Remaining cells were washed 3× with 1 ml sterile DNase/RNase free double distilled water (Life Technologies, Burlington, ON, Burlington, ON) [9]. Cells were resuspended in 500 μl sterile PBS for storage prior to DNA extraction. The extraction protocol was outlined in Faria et al., (2015) and included extensive cell lysis using both lysozyme and mutanolysin (Sigma-Aldrich, Oakville, ON), RNaseA treatment (Life Technologies, Burlington, ON), proteinase K treatment (Invitrogen, Life Technologies, Burlington, ON) and DNA separation with phenol-chloroform-isoamyl (Life Technologies, Burlington, ON). Final DNA concentration and purification was done using the Zymo DNA Clean & Concentrator™-25 (Zymo Research, Irvine, CA) column containing 200 μl of ChIP DNA Binding Buffer (Zymo Research, Irvine, CA) [9].

16S rRNA gene bacterial community profiling with paired-end Illumina

Bacterial profiling of the v3 variable region of the 16S rRNA gene was carried out as described previously [9]. The primers used with modifications including the addition of Illumina multiplexing, bridge amplification and sequencing regions were 341F (5’CCTACGGGAGGCAGCAG3’) and 518R (5’ATTACCGCGGCTGCTGG3’) [9]. The resulting PCR products were amplified in triplicate as previously outlined [9]. Samples were sequenced using the Illumina MiSeq personal sequencer (Illumina Incorporated, USA) at the McMaster Genomics Facility (Hamilton, ON, Canada) and image analysis, base calling, and error estimation were completed using the Illumina Analysis Pipeline (version 2.6) [16]. Briefly, pooled DNA libraries were tested with the Agilent BioAnalyzer High Sensitivity DNA chip. qPCR was performed as previously described [9, 17]. The 16S rRNA gene v3 region pools were then sequenced, using previously published primers, in the forward and reverse direction on the Illumina MiSeq instrument [9, 17, 18]. Illumina’s Casava software (version 1.8.2) was used to demultiplex each run [9, 17]. Each illumina run included a no-template control sample as an internal control to ensure there was no contamination. The sequencing data was processed with custom, in-house standardized workflow and Perl scripts [9, 18]. Primer removal and trimming was carried out with Cutadapt [19] and paired-end sequences alignment and quality filtering was carried out using PANDAseq [20]. Chimera, singletons, contamination and human DNA was removed during the data filtering steps. The “noRoot” OTU was removed as it represented non-bacterial DNA amplification due to well document 16S primer cross-reactivity to human DNA [21]. Please refer to Additional file 1: Table S1 for OTUs removed during filtering. Following filtering of these sequences, a cut-off of 500 reads per sample was applied as the lowest level of abundance required for analysis.

Taxonomic identification and diversity measures

Taxonomic summaries and subsequent analysis were done using QIIME version 1.7.0 [22]. Operational taxonomic units (OTUs) clustering and analysis of taxonomic summaries was done as previously described [9]. Briefly, operational taxonomic units (OTUs) clustering at a threshold of 97% sequence similarity was carried out using AbundantOTU+ [23]. Taxonomic identification was assigned using the Ribosomal Database Project classifier [24] using the Greengenes reference database, February 4th 2011 release [25] as a training set. QIIME computational analysis pipeline was used for community analysis [22]. Beta-diversity was used to examine variation between DNA profiles from different samples. Both weighted and unweighted UniFrac distances were used for clustering of the samples which were visualised using principal coordinate analysis (PCoA) [26, 27]. KiNG version 2.21 visualization software was used for PCoA plots [28]. Composite unweighted pair group method with arithmetic mean (UPGMA) hierarchal clustering of the sequencing data was done with weighted Unifrac distance metrics. Jackknife beta-diversity on evenly re-sampled OTU tables was applied using weighted UniFrac distance to validate the strength of UPGMA clustering [26].

The representative sequence for each OTU was also aligned to 16S rRNA sequences using the HOMD database (www.homd.org) and to the National Center for Biotechnology-Basic Local Alignment Search Tool (NCBI-BLAST, http://blast.ncbi.nlm.nih.gov/Blast.cgi) megablast nucleotide search tool.

In addition, PCR and illumina sequencing was performed on all the reagents and buffers used in the saponin blood-treatment and the DNA extraction protocol. These results were previously discussed [9].

Results

Patient demographics and admission results

Based on sequencing criteria discussed in the next section, not all patient blood samples processed were included in the analysis. Of the 52 ED blood samples collected, 12 were analyzed (mean age 50 years (± 13.18 SD). The predicted sources of infection were lung (4/12), genitourinary (2/12), skin soft-tissue (2/12), joint/bone (1/12), endovascular (2/12), and one unknown (Table 1). From the pediatric ED blood samples cohort, 9 of 28 samples were analyzed (mean age + 4 years (± 2.87 SD). The predicted sources of sepsis were pneumonia (3/9), intra-abdominal infection (3/9), meningitis (1/9), and two unknown (Table 1). A positive blood culture was identified in 67% of the adult ED patients and in 11% of the pediatric ED patients included in this study (Table 1). The healthy blood samples came from healthy adults and were discussed previously [9].
Table 1

Demographics of samples collected from adult ED (FED) patients and samples collected from pediatric ED (AERG) patients

Patient

Age Range

Gender

SIRS (1–4)

Primary Focus of Infection

Blood Culture

Top OTU (s)

Adult ED Samples

 FED31

30–40

M

2

Endovascular

Negative

Anaerococcus, Staphylococcus

 FED56

70–80

M

4

Skin or soft tissue

Unknown

Bacillaceae

 FED7

30–40

M

4

Catheter related

Staphylococcus aureus

Staphylococcus

 FED36

50–60

M

4

Endovascular

Serratia marcescens

Serratia

 FED14

40–50

F

3

Skin or soft tissue

Staphylococcus aureus

Streptococcus, Gammaproteobacteria

 FED42

50–60

M

2

Lung

Streptococcus pneumoniae

Escherichia, Gammaproteobacteria

 FED39

60–70

F

3

Unknown

Group B Streptococcus

Escherichia, Streptococcus

 FED44

40–50

F

2

Lung

Streptococcus pneumoniae

Enterobacteriaceae, Klebsiella

 FED15

60–70

M

2

Bone/Joint

Negative

Streptococcus, Bacillus

 FED4

40–50

F

4

Urinary Tract

Escherichia coli

Gammaproteobacteria

 FED57

40–50

F

3

Lung

Unknown

Streptococcus, Actinomycetales

 FED34

40–50

M

3

Lung

Streptococcus pneumoniae

Lactococcus, Streptococcus

Pediatric ED Samples

 AERG2.106

2–3

F

2

Pneumonia

Negative

Streptococcus, Escherichia, Staphylococcus

 AERG2.102

4–5

F

3

Appendicitis

Negative

Staphylococcus, Streptococcus

 AERG2.113

4–5

F

3

Meningitis

Negative

Staphylococcus, Streptococcus

 AERG1.106

2–3

F

2

Pneumonia

Negative

Enterobacteriaceae, Streptococcus

 AERG2.043

7–8

F

2

Appendicitis

Negative

Staphylococcus

 AERG2.076

10–11

F

2

Duplicate Cyst

Negative

Staphylococcus

 AERG2.205

2–3

M

2

Pneumonia

Gram-positive cocci resembling Staphylococcus

Bacillaceae, Staphylococcus, Moraxella, Enterococcus, Clostridium

 AERG2.235

No data

No data

No data

No data

Negative

Bacillaceae, Staphylococcus, Moraxella, Enterococcus, Clostridium

 AERG2.198

3–4

F

4

No data

Negative

Bacillaceae, Staphylococcus, Moraxella, Enterococcus, Clostridium

Of the 116 ICU patient blood samples collected, 54 were used for analysis based on parameters for DNA sequencing depth outlined in a subsequent section. The clinical data upon ICU admission for the 54 patient samples used included the patients’ age, sex, APACHE II score, SOFA score, the ICU length of stay (LOS), and outcome (Table 2). Summary statistics of clinical parameters for ICU patients is available in Additional file 2: Table S2. Briefly, the mean age was 58 years (SD 15.62) with 51.9% of patients being male. The average admitting APACHE II score, a measure of disease severity [14], was 22.9 (SD 7.1). The SOFA score, a measure of organ failure [15], average was 10 (SD 4.1). With respect to mortality, 9 of 54 (17%) died during their admission. The principal sources of infection were lower respiratory tract infections n = 18 (33%) patients and gastrointestinal infections n = 16 (30%); there were n = 4 (7%) who had septic shock. A positive blood culture result was present in 30% of the ICU patients included in this study.
Table 2

Admissions data for the adult ICU patients in Groups 1–3

Sample

Age Range

Gender

Admitted From

Admitting Diagnosis

Admitting APACHE II

Max SOFA

ICU Outcome

Group 1-A

 ASN455

40–50

F

In-patient

Sepsis-Unknown

29

17

Dead

 ASN350

40–50

M

Other

Bacterial pneumonia

19

18

Dead

 ASN349

60–70

F

In-patient

Intracranial abscess

26

8

Alive

 ASN452

70–80

M

ED

Bacterial pneumonia

13

5

Alive

 ASN469

60–70

M

OR

Surgery for cellulitis

27

10

Alive

 ASN470

60–70

M

In-patient

Sepsis-Gastrointestinal

34

17

Dead

 ASN465

70–80

F

In-patient

Cardiac arrest, post-kidney transplant

28

12

Alive

 ASN463

70–80

F

In-patient

Congestive heart failure

28

7

Alive

Group 1-B

 ASN366

50–60

M

OR-Emergency

Tonsil or pharyngeal infection

19

10

Alive

 ASN357

80–90

F

Other

Septic shock

28

8

Alive

 ASN376

20–30

M

OR-Emergency

Haemothorax or haemopneumothorax

20

7

Alive

 ASN368

60–70

M

OR-Emergency

Leaking biliary anastamosis

11

7

Alive

 ASN294

60–70

F

Other

Self poisoning with sedatives or hypnotics

10

8

Alive

Group 2- AI

 ASN167

30–40

F

ED

Hepatic abscess

15

5

Alive

 ASN168

50–60

F

In-patient

Bacterial pneumonia

31

11

Alive

 ASN475

70–80

F

In-patient

Gastrointestinal abscess

18

7

Alive

 ASN438

50–60

F

ED

Pneumonia-Other

21

6

Alive

 ASN429

70–80

F

In-patient

Respiratory cause

16

10

Alive

 ASN315

20–30

M

OR-Emergency

Necrotizing fasciitis and septic shock

7

5

Alive

 ASN363

30–40

M

ED

Septic shock

16

12

Alive

Group 2-AII

 ASN338

20–30

M

OR-Emergency

Traumatic rupture or laceration of liver

30

15

Alive

 ASN300

60–70

F

OR-Emergency

Small bowel infarction

24

16

Alive

 ASN292

60–70

M

OR-Emergency

Septic shock

34

16

Alive

 ASN297

50–60

F

OR-Emergency

Oesophageal or gastro-oesophageal tumour

24

10

Alive

 ASN328

20–30

M

ED

Self poisoning with narcotics

22

11

Alive

 ASN473

70–80

M

ED

Bacterial pneumonia

32

11

Dead

 ASN420

50–60

F

ED

Bacterial pneumonia

10

6

Alive

Group 2-AIII

 ASN379

70–80

M

In-patient

Pneumonia

28

12

Alive

 ASN371

50–60

M

Other

Bleeding duodenal ulcer

21

1

Alive

 ASN381

50–60

F

OR-Emergency

Necrotizing fasciitis and bacterial pneumonia

16

4

Alive

 ASN432

50–60

F

ED

Bacterial pneumonia

20

8

Alive

 ASN444

70–80

F

ED

Emphysema/bronchitis

24

4

Alive

 ASN340

60–70

M

ED

Cutaneous cellulitis

15

12

Alive

 ASN339

30–40

F

In-patient

Intracranial abscess

23

11

Alive

 ASN343

50–60

M

In-patient

Inhalation pneumonitis (gastrointestinal contents)

27

8

Alive

Group 2-AIV

 ASN415

70–80

F

ED

Pneumonia-Other

30

9

Alive

Group 2-B

 ASN479

50–60

M

ED

Septic arthritis

19

11

Alive

Group 3-A

 ASN458

60–70

M

OR-Emergency

Bacterial pneumonia and cardiovascular surgery

40

20

Dead

 ASN436

70–80

M

ED

Sepsis-Gastrointestinal

23

8

Alive

 ASN440

60–70

F

In-patient

Congestive heart failure and emphysema/bronchitis

28

12

Alive

 ASN451

70–80

F

OR-Emergency

Surgery for gastrointestinal perforation/rupture

27

14

Alive

 ASN409

50–60

M

In-patient

Respiratory cause

22

10

Alive

 ASN418

40–50

F

ED

Surgery for (resection) gastrointestinal vascular ischemia,

26

10

Dead

 ASN454

70–80

M

ED

Upper gastrointestinal bleeding

NDa

4

Dead

 ASN408

40–50

M

In-patient

Surgery for abdomen-trauma

15

5

Alive

 ASN434

50–60

M

In-patient

Sepsis-Unknown

26

12

Alive

 ASN424

60–70

M

In-patient

Surgery for (resection) gastrointestinal vascular ischemia,

14

12

Dead

 ASN464

60–70

M

OR-Emergency

Surgery for gastrointestinal perforation/rupture

26

12

Alive

 ASN348

70–80

F

OR-Emergency

Septic shock

27

12

Alive

 ASN466

70–80

F

In-patient

Surgery for cholecystectomy/cholangitis (gallbladder removal)

31

9

Alive

 ASN461

20–30

F

ED

Bacterial pneumonia

19

12

Alive

Group 3-B

 ASN476

60–70

M

In-patient

Septic arthritis

31

9

Alive

 ASN474

40–50

F

In-patient

Sepsis-Pulmonary

29

19

Dead

 ASN477

50–60

M

ED

Bacterial pneumonia

17

9

Alive

aNo Data

Bacterial DNA profiles of blood of septic ICU patients clustered into three groups

Prior to analysis, sequencing data was filtered to remove low diversity samples. After this the counts per sample were a minimum of 151, maximum of 41,7190 with a median of 1795.5 and mean count per sample of 14,725. Following the removal of the “noRoot” OTU, singletons, and known contaminant OTUs, the number of counts per sample decreased (Table 3). The OTUs removed from the analysis are available in Additional file 2: Table S2.
Table 3

Clusters, Clinical Microbiology, and OTU analysis of the adult ICU patient blood samples

Sample

Blood Culture

Other Culturea

“noRoot” OTU %

Top OTU(s)b

RepresentativeSequenceIDc

Group 1d-A

 ASN455

Group G Streptococcus

Group G Streptococcus

98.8

175

Streptococcus dysgalactiae/agalactiae

 ASN350

Negative

Negative

97.2

5

Streptococcus pneumoniae/oralis/mitis

 ASN349

Negative

Streptococcus intermedius

99.4

5

Streptococcus pneumoniae/oralis/mitis

 ASN452

Negative

Not Done

98.9

8

Streptococcus intermedius/anginosus

 ASN469

Campylobacter ureolyticus, Fusobacterium species

Enterococcus

92.8

8

Streptococcus intermedius/anginosus

 ASN470

VRE

VRE

74.8

8,5

Streptococcus intermedius/anginosus, Streptococcus pneumoniae/oralis/mitis

 ASN465

Negative

VRE

95.4

8

Streptococcus intermedius/anginosus

 ASN463

Negative

Not Done

97.4

8

Streptococcus intermedius/anginosus

Group 1-B

 ASN366

Negative

Streptococcus anginosus, Prevotella species, CoNS

91.9

2,5

Staphylococcus aureus, Streptococcus pneumoniae/oralis/mitis

 ASN357

Negative

VRE

97.8

5

Streptococcus pneumoniae/oralis/mitis

 ASN376

Not Done

Not Done

90.1

5,2

Streptococcus pneumoniae/oralis/mitis, Staphylococcus aureus

 ASN368

Negative

Klebsiella pneumoniae, Haemophilus parainfluenzae, Prevotella species

94.8

5,2

Streptococcus pneumoniae/oralis/mitis, Staphylococcus aureus

 ASN294

Staphylococcus aureus

Staphylococcus aureus

94.9

5

Streptococcus pneumoniae/oralis/mitis

Group 2-AI

 ASN167

Negative

SMG

94.1

32

Gammaproteobacteria

 ASN168

Not Done

Fungal

96.9

32

Gammaproteobacteria

 ASN475

Pseudomonas aeruginosa

Pseudomonas aeruginosa

98.0

15,32, 48

Proteobacteria, Gammaproteobacteria, Pseudomonas sp.

 ASN438

Negative

Legionella pneumophila

98.4

3,4,379

Enterobacter sp., Klebsiella sp., Legionella sp.

 ASN429

Negative

Legionella pneumophila

87.1

3

Enterobacter sp.

 ASN315

Negative

Not Done

83.3

3

Enterobacter sp.

 ASN363

Staphylococcus aureus, Gram-negative bacilli

Staphylococcus aureus

0.72

11

Serratia marcescens

Group 2-AII

 ASN338

Not Done

Not Done

96.8

2,101

Staphylococcus aureus, Anaerococcus sp.

 ASN300

Negative

Fungal

92.5

32

Gammaproteobacteria

 ASN292

Negative

Not Done

91.0

6,76,125

Bacillus sp., Lachnospiraceae, Bacillus sp.

 ASN297

Fungal

Fungal

92.1

40,15, 125,

Streptococcus sp., Proteobacteria, Bacillus sp.

 ASN328

Negative

Staphylococcus aureus, Streptococcus pneumoniae

94.9

2

Staphylococcus aureus

 ASN473

Pseudomonas aeruginosa

Not Done

92.5

15

Proteobacteria

 ASN420

Negative

Not Done

97.5

2,59

Staphylococcus aureus, Proteobacteria

Group 2-AIII

 ASN379

Negative

Not Done

77.5

2

Staphylococcus aureus

 ASN371

CoNS

Not Done

93.8

2,5,13

Staphylococcus aureus, Streptococcus pneumoniae/oralis/mitis, Escherichia coli

 ASN381

Negative

Legionella pneumophila

94.0

13,3,2

Escherichia coli, Enterobacter sp., Staphylococcus aureus

 ASN432

Negative

Group A Streptococcus

95.2

8,2,3

Streptococcus intermedius/anginosus, Staphylococcus aureus, Enterobacter sp.

 ASN444

Negative

Not Done

99.2

8,3

Streptococcus intermedius/anginosus, Enterobacter sp.

 ASN340

Group C Streptococcus

Not Done

99.3

2,13

Staphylococcus aureus, Escherichia coli

 ASN339

Negative

Not Done

98.6

2,3

Staphylococcus aureus, Enterobacter sp.

 ASN343

Negative

Fungal

99.7

2,15

Staphylococcus aureus, Proteobacteria

Group 2-AIV

 ASN415

Negative

Not Done

93.3

97

Prevotella melaninogenica

Group 2-B

 ASN479

Negative

Finegoldia magna

99.2

81

Finegoldia magna

Group 3-A

 ASN458

MRSA

MRSA

84.2

2

Staphylococcus aureus

 ASN436

Negative

VRE

92.5

2

Staphylococcus aureus

 ASN440

Enterococcus faecium

Not Done

97.2

2

Staphylococcus aureus

 ASN451

Not Done

Not Done

99.5

2

Staphylococcus aureus

 ASN409

Not Done

Not Done

98.6

2

Staphylococcus aureus

 ASN418

Not Done

Not Done

87.1

2

Staphylococcus aureus

 ASN454

Negative

Not Done

98.4

2

Staphylococcus aureus

 ASN408

Not Done

Not Done

80.2

2

Staphylococcus aureus

 ASN434

Pseudomonas aeruginosa

Not Done

8.3

2

Staphylococcus aureus

 ASN424

Bifidobacterium species

Not Done

88.8

2

Staphylococcus aureus

 ASN464

Not Done

Not Done

76.2

2,8,5

Staphylococcus aureus, Streptococcus intermedius/anginosus, Streptococcus pneumoniae/oralis/mitis

 ASN348

Negative

Micrococcus species, Streptococcus viridians group

98.2

2,5

Staphylococcus aureus, Streptococcus pneumoniae/oralis/mitis

 ASN466

Negative

CoNS, Coryneform bacilli, Candida parapsilosis

92.8

2,8,13

Staphylococcus aureus, Streptococcus intermedius/anginosus, Escherichia coli

 ASN461

Fusobacterium necrophorum

Fusobacterium

90.8

2,72

Staphylococcus aureus, Fusobacterium

Group 3-B

 ASN476

Escherichia coli

Not Done

80.2

6,2,19

Bacillus sp., Staphylococcus aureus, Lysinibacillus sp.

 ASN474

Negative

Not Done

32.6

6,2,17

Bacillus sp., Staphylococcus aureus, Moraxella sp.

 ASN477

Negative

Not Done

96.6

6,2,32

Bacillus sp., Staphylococcus aureus, Gammaproteobacteria

aRefers to any other clinical diagnostic culture results that pertained to that patient within 24 hours of whole blood collection

bOTU number that represented most sequences identified and aligned in the Illumina analysis

cResults from alignment of the top OTU representative sequence to curated 16S rRNA databases

dBased on composite UPGMA trees generated using weighted UniFrac and jackknife resampling

Phylogenetic relationships in the ICU patient samples with at least 500 sequences were analyzed. Beta-diversity was assessed using jackknife analysis equally resampled OTU tables to ensure clustering was consistent [27]. Hierarchal clustering based on UniFrac was visualized as UPGMA phylogenetic trees, a well supported method for visualization of next-generation sequencing data [9, 17, 18, 22]. Due to low sequencing depth, 62 patient samples were not clustered. The taxonomic profiles of the remaining 54 patients (of the original 116) samples with a sequencing depth above 500 were clustered into three main groups (Fig. 1a). As indicated, the “noRoot” OTU was removed from analysis as it represented human DNA. When the “noRoot” OTU sequence was aligned in the NCBI-BLAST database the alignments were to mitochondrial DNA or to eukaryotic sequences. The proportion of non-bacterial “noRoot” OTU in the septic blood samples ranged from 99.98 to 0.007% with the average being 92.4% (Table 3).
Fig. 1
Fig. 1

Taxonomic profiles of whole blood samples from septic ICU patient. Septic whole blood samples collected from ICU patients clustered into three groups based on their taxonomic bacterial DNA profiles. Taxonomic profiles of whole blood samples with 500 or more sequences and clustered using weighted UniFrac (54 patients). A composite unweighted pair group method with arithmetic mean (UPGMA) tree of all the samples was generated with the profiles ordered based on their placement in the UPGMA tree (a). Three groups of SB samples were clearly identified. Group 1 was defined by the abundance of Streptococcus in the profile, Group 2 by the abundance of Gram-negative OTUs, and Group 3 by the abundance of Staphylococcus. Blood culture results for each sample are indicated below the sample. Blood culture positive but discordant from molecular sequencing are indicated by (+), blood culture positive with concordance to sequencing by (a red +), and blood culture negative (−). Samples with a (*) are those with molecular profile results that are supported by other clinical culture data. The average taxonomic profile for the cluster groups shows the breakdown of the bacterial DNA distribution in each taxonomic cluster group (b)

For the 54 samples used in the analysis, the sequences per sample ranged from a minimum number of sequences per sample of one and a maximum of 166,596. OTUs that were detected less than 10 times in the population were excluded resulting in 460,386 sequences representing 355 OTUs. These OTUs clustered into 141 taxonomically distinct groups with the reference sequence reflecting the maximum level in which the RDP Classifier would, with confidence, identify the OTU [24].

Three clusters of DNA profiles were identified in the ICU sample cohort (Fig. 1a). Group 1 OTU profiles were distinguished by the abundance of Streptococcus DNA with two clades. The Group 1A samples had 65% or higher relative abundance of Streptococcus and the Group 1B samples with less than 65% but greater than 30% Streptococcus DNA (Fig. 1b). Using the representative sequence for each dominate OTU, further classification of the Streptococcus was predicted. Four of the patients had species of Streptococcus Mitis Group (S. pneumoniae/mitis/oralis) as the principal OTU, four had the Streptococcus Anginosus/Milleri group as the principal OTU, one had a Streptococcus dysgalactiae/agalactiae OTU, and an additional three had a similar abundance of a Streptococcus Mitis Group OTU and Staphylococcus aureus OTUs, and one patient had similar abundance of the Streptococcus Anginosus/Milleri OTU and the Streptococcus Mitis Group (Table 3).

Group 2 ICU patient blood samples had the greatest diversity in terms of taxonomic representation (Fig. 1a). A unifying trend for Group 2 was abundance of OTUs representing Gram-negative bacteria (Fig. 1b). Group 2 was further subdivided into two clades with Group 2A having four sub-groups I, II, III, and IV (Fig. 1b). In Group 2AI, most the DNA diversity was represented by the Gammaproteobacteria, Proteobacteria, and Pseudomonas taxonomic groups in the first clade whereas Group 2AII were represented by the Enterobacteriaceae and Klebsiella DNA (Table 3). Within Group 2AI, there was one blood sample in which the Serratia taxon represented 100% of the relative DNA abundance (Fig. 1b). There was only one Serratia OTU present in the SB samples and the representative sequence aligned to the Serratia marcescens 16S rRNA gene (Table 3). There was also one sample in Group 2AI, ASN438, in which Legionella DNA represented 25% of the relative DNA abundance. This was the only ICU patient where Legionella DNA was recovered (Fig. 1b). The Group 2AII samples had greater taxonomic diversity and the principal OTUs identified in the Group 2AII samples had sequence identities matching Bacillus, Gammaproteobacteria, Lachnospiraceae, Xanthomonadaceae, and Staphylococcus (Table 3). The Group 2AIII isolates had a mix of OTUs representing both Gram-positive and Gram-negative bacteria in equal proportions (Fig. 1a-b). Group 2AIV consisted of one patient blood sample in which the abundance of the Prevotella DNA, at 30%, separated in from the other Group 2 samples (Fig. 1b). Group 2B was also represented by a single sample where Finegoldia DNA represented 76% of the OTU abundance (Fig. 1b). The Finegoldia OTU aligned to Finegoldia magna (Table 3).

The third cluster of ICU blood samples grouped based on the Staphylococcus DNA abundance (Fig. 1a). Group 3A consisted of blood samples in which Staphylococcus represented 37–75% of the bacterial DNA amplified (Fig. 1a). The majority of samples had a Staphylococcus OTU that aligned to S. aureus (Table 3). The Group 3B clade was distinguished from 3A by the Bacillaceae and Moraxella DNA representing 25–41% and 5–14% of the molecular profiles (Fig. 1b). This was also the only group in which Clostridium and Enterococcus DNA were amplified to a detectable level in the taxonomic profiles (Fig. 1a).

Lastly, the 62 low sequence depth samples were assessed. Principal coordinates analysis (PCoA) done on low sequence depth SB samples indicated that the majority of the low sequence depth samples aligned with the three clusters of ICU patient blood samples analyzed with 12 outliers detected (Fig. 2).
Fig. 2
Fig. 2

PCoA of SB samples that had low sequencing depth indicate they cluster mainly with the Group 2 samples. Principal coordinates analysis, based on weighted UniFrac was done for all blood samples from the ICU patient cohort (n = 116). Of these samples, 54 were used to distinguish DNA profiles into three clusters; Group 1A (orange) and Group 1B (green); Group 2AI (purple), Group 2AII (yellow), Group 2AIII (light blue), Group 2AIV (turquoise), and Group 2B (pink); and Group 3A (grey), and Group 3B (brown). The remaining 62 samples (dark blue) were overlapped with the cluster groups. Circles were added to visualize the area in the PCoA plot that each cluster group isolates occupied. The majority of the low sequence depth samples had bacterial DNA profile profiles that clustered with the Group 2 ICU blood samples and a limited number showing similarity to Group 1 (n = 11) or Group 3 (n = 8) ICU blood samples. There were 12 blood samples of low sequencing depth that did not overlap with any of the ICU blood sample clusters

Correlation of bacterial DNA profiles to clinical microbiology data from septic ICU patients

The conventional blood culture results for the ICU patients were compared to the molecular profiles obtained in this study (Table 3). Of the 54 patients clustered, blood culture results were obtained for 46 patients with only 15 (33%) having a positive blood culture result. There was limited concordance between molecular profiling and blood culture data which was present in 5 samples (Fig. 1a). In contrast, concordance between molecular profiles and primary infection sample results was noted in several cases discussed below.

The blood sample from ASN455 had Streptococcus DNA representing over 75% of the bacterial DNA amplified (Table 3). The representative sequence ID for the Streptococcus OTU in this sample aligned to S. agalactiae/dysgalactiae, which are typically Group G Streptococcus [29]. This correlated with the clinical blood culture results which indicated Group G Streptococcus was cultivated (Table 3).

In the ASN363 sample, the Serratia OTU represented 100% of the relative DNA abundance (Table 3) whereas diagnostic blood culture indicated a S. aureus infection with Gram-negative bacilli (Table 2). Given the molecular profiling data, it could be hypothesized that the Gram-negative bacilli that failed to grow were S. marcescens.

ASN438 was a blood sample from a patient who was known to have a L. pneumophila pneumonia as part of the documented Legionella outbreak within the Calgary Health Region in November–December of 2012. Pleural fluid culture results for this patient were positive for L. pneumophila empyema yet blood culture was negative (Table 3). However, the molecular profiling data included the Legionella OTU providing evidence that L. pneumophila was likely in the bloodstream but below the threshold level to be recovered by blood culture diagnostics.

Patient ASN479 in which clinical diagnostic blood cultures were negative yet the Finegoldia OTU was identified in molecular profiling of their blood sample. Given the presence of F. magna cultured from the patient’s septic joint fluid, the molecular profiling data was suggestive of a F. magna bloodstream infection (Table 3).

Lastly, ASN458 patient had MRSA identified from blood culture as well as their predicted primary infection sample (Table 3). The molecular profiling of the ASN458 blood sample indicated the principal OTU had sequence alignment to S. aureus indicating a correlation between the molecular profiling results and the clinical culture data.

Overall, these cases highlighted how next-generation sequencing of DNA from septic patients could be used to detect clinically significant infections as the results correlated with the clinical data. A unifying trend was the implication of haematogenous spread of bacteria from the primary infection sources into blood even if blood cultures were negative.

Bacterial DNA profiles from septic blood were distinct from healthy controls

Prior to applying this method to clinical samples, intense analysis was done to ensure bacterial DNA recovered was not a result of contamination [9]. As reported previously, DNA profiles obtained from healthy adult blood samples clustered separately from blood samples from septic ICU patients [9]. Further, the addition of the healthy blood samples to the analysis did not impact the phylogenetic tree structure distribution for cluster Group 1 or Group 3 (Fig. 3). Based on this, the bacterial DNA profiles in these groups were considered as potential bloodstream infections and not contamination. For Group 2, clusters remained intact in terms of the distribution of samples and the branching within the tree except for Group 2AIII. These samples were distinguished from the healthy control samples by the prevalence of certain OTUs including Fusobacterium, Neisseria, and Anaerococcus in the last three patients (Fig. 3). The Group 2AIII blood samples were statistically distinct from the healthy blood samples in phenetic diversity based on weighted UniFrac (PERMANOVA, p = 0.001) [9]. Despite this, the clustering of their molecular profiles with the control samples limited interpretation of the DNA profiles. These patients perhaps had lower bacterial DNA abundance in the sample thereby increasing the relative abundance of the contaminants in the taxonomic profile. Caution was used in the interpretation of the data such that OTU prevalence was considered significant when there was supporting clinical information.
Fig. 3
Fig. 3

Bacterial DNA profiles from healthy adult blood samples clustered together and were phylogenetically distinct from the bacterial DNA profiles identified in blood samples from septic ICU patients. A composite unweighted pair group method with arithmetic mean (UPGMA) phylogeny of all the samples was generated with from the jackknife, weighted UniFrac beta-diversity comparison of the DNA profiles from septic ICU patient samples and healthy adult blood samples. The ICU adult blood samples were labeled based on the cluster groups identified in Fig. 1. The taxonomic cluster group’s 1A, 1B, 2AI, 2AII, 2AIV, 3A, and 3B remained intact when clustered with healthy adult blood samples. The DNA profiles from healthy adult blood samples clustered together and were a distinct clade which divided the Group 2AIII cluster. Despite this, the addition of the healthy adult blood samples did not impact the tree structure as the distribution of all three clusters of bacterial DNA profiles from septic ICU patient samples was preserved

Common bacterial DNA patterns existed across adult and pediatric sepsis patients from the ED

In addition to the septic patients from the ICU, blood samples were also collected from adult and pediatric patients presenting in the Emergency Department (ED) that were suspected of sepsis. Twelve of these were analyzed further. The rationale was to determine if these ED patients had bacterial DNA profiles similar those patients admitted to ICU with clinically confirmed sepsis. The bacterial profiles from the ED patients cluster with the ICU samples into the groups described in Fig. 1 and distinct from the healthy controls (Fig. 4). As seen with the ICU patient cohort, the clusters were defined by abundance of Streptococcus OTUs, Gram-negative OTUs, or Staphylococcus OTUs. Nine ED sample profiles clustered with the Gram-negative dominant samples whereas two ED samples grouped with the Staphylococcus and one ED sample grouped with the Streptococcus dominant samples, respectively (Fig. 4).
Fig. 4
Fig. 4

The bacterial DNA profiles of ICU and ED blood samples clustered together and separately from healthy adult blood samples. Taxonomic bacterial DNA profiles were summarized for all whole blood samples with 500 or more sequences. A composite unweighted pair group method with arithmetic mean (UPGMA) phylogeny of all the samples was generated with the profiles ordered based on their placement in the phylogenetic tree and clustered using weighted UniFrac. The samples clustered into 5 branches on the phylogenetic tree

Discussion

The use of Illumina sequencing technology combined with a novel DNA recovery method enabled the characterization of bacterial DNA isolated from 3 to 5 ml blood samples collected from several cohorts of septic patients. Among these cohorts, the samples from patients admitted to ICU with sepsis had the highest number of samples available to examine trends. Analysis of the bacterial DNA profiles, presented as a proportion of total bacterial DNA, indicated that three common distributions were present in these samples. Association with the infection source, based on the admission diagnosis, showed the strongest correlation to the bacterial DNA profiles. The Group 1 bacterial DNA profile had OTUs representative of commensal microbiota from the upper respiratory tract or the skin in addition to Streptococcus as the predicted pathogen (Fig. 1, Table 2). Many of the patients in Group 1 were admitted with pneumonia, upper respiratory tract infections, abscess and cellulitis. Streptococcus species are recognized principal pathogens in these clinical presentations [2935]. The Group 2 patients had diverse clinical presentations and bacterial DNA profiles representing Gram-negative organisms (Table 2, Table 3, Fig. 1b). Patients admitted with gastrointestinal infections or trauma likely developed sepsis from gastrointestinal microbiota including known Gram-negative opportunistic pathogens [36]. The remaining patients within Group 2 with abscesses or airway infections had bacterial DNA OTUs that correlated to upper airway and skin associated microbiota [31, 33, 3741]. The Group 3 bacterial DNA profiles were distinguished by the large proportion of Staphylococcus OTUs (Fig. 1). These samples were obtained from patients admitted for emergency surgical interventions, joint infections, and pneumonia (Table 2). Again, the role for Staphylococcus as a clinical pathogen in such presentations of sepsis is well documented [31, 37, 39, 4250]. Taken together, these data support the interpretation of these bacterial DNA profiles as representation of bacterial bloodstream infections with DNA from known pathogenic organisms recovered that correlated to the patient’s clinical presentation at the time of enrollment in the study.

The molecular profiling results provided more evidence of sepsis bloodstream infection when compared to the conventional diagnostic blood culture. For the adult ICU blood samples included in the analysis, only 33% had a positive clinical blood culture (Table 2). In comparison, bacterial DNA was recovered from all these blood samples and the bacterial DNA profiles in these samples were distinct from those recovered from the blood of healthy adult control samples (Fig. 3). While the presence of bacterial DNA in these blood samples did not indicate the presence of viable organisms, it suggested that the clinical blood cultures were under-representing the presence of bloodstream infections in this cohort. These results are comparable to similar studies using molecular diagnostic platforms have also reported under representation of bloodstream infections when blood culture diagnostics were compared to PCR based methods [13, 5155]. This study also outlined several cases where the bacterial DNA amplified from the blood sample had good concordance with bacterial pathogens that were recovered from pertinent clinical diagnostic cultures. Overall, combining our molecular profiling analysis of the bacterial DNA patterns with a chart review of the patient clinical data including culture-based diagnostic results strengthened our interpretations of the molecular profiling results and further demonstrated the potential for this molecular-based approach to augment culture-based microbial diagnostic results.

This study also demonstrated that the bacterial DNA patterns were conserved across various subsets of septic patients. Indeed, there was similar clustering of all the clinical blood samples regardless of the patient’s presentation to ICU or ED and across both adult and pediatric cohorts (Fig. 3). In addition, this analysis confirmed two principal bacterial DNA patterns seen in the septic ICU cohort; one in which Streptococcus DNA was the most prevalent and one in which Staphylococcus DNA was the most prevalent (Fig. 3). Further analysis of the OTU distribution of Streptococcus indicated that the principal predicted Streptococcus species found in whole blood were the Streptococcus Anginosus/Milleri Group and Streptococcus Mitis Group (S. pneumoniae/mitis/oralis) at 33.5 and 10.59% respectively (Additional file 3: Table S3). The prevalence of the Anginosus/Milleri Group superseding that of Mitis Group was not expected given many studies suggesting S. pneumoniae as a principal pathogen recovered in clinical diagnostic blood culture positive bloodstream infections [56]. A recent study indicated 89% of culture-positive bloodstream infections were a result of S. pneumoniae [57] whereas the Anginosus/Milleri Group represented a smaller proportion of the Streptococcus bloodstream infections [49]. As such, these results suggested greater diversity of Streptococcus species in sepsis bloodstream infections than previously considered based on blood culture diagnostics. Interestingly, other studies using targeted culturing and culture-independent approaches have also demonstrated a role for the Anginosus/Milleri Group in human infections [5861]. This study now adds new data to suggest a greater role for this group in acute bloodstream infections than previously reported [8, 49, 57].

For Staphylococci, the OTU with sequence alignment to S. aureus represented 97% of the Staphylococcus OTUs present in the septic ICU population (Additional file 3: Table S3). Most reports from clinical diagnostic blood culture confirmed bloodstream infections indicate S. aureus as the second most commonly isolated organism [56]. S. aureus predominate blood samples were obtained from patients with documented surgical infections (11/17), respiratory infections (5/17), and septic arthritis (1/17). Post-operative Staphylococcus infections have been documented in other literature reports [62, 63] and S. aureus is a common pathogen in respiratory infections and septic joint infections [50]. The remaining 3% of Staphylococcus OTUs aligned with CoNS (Additional file 3: Table S3). Most population-based assessments cluster the CoNS bloodstream infections together since clinical laboratories don’t distinguish these organisms beyond this level [49, 64]. Taken together, the molecular profiling data suggested that there might be a larger role for diverse CoNS in sepsis than is currently appreciated using clinical diagnostic blood culture approaches.

Overall, this study demonstrated the potential strengths of the molecular profiling data when evaluated alongside the patient’s admissions data and, to some extent, their culture data. The results indicated Streptococcus and Staphylococcus as principal pathogens in sepsis bloodstream. However, the prevalence of polymicrobial DNA in whole blood from septic patients suggested there could be greater propensity for polymicrobial infections in sepsis than currently appreciated using cultivation-dependent and broth-enrichment based approaches. Similar results from direct blood analysis have shown utility of molecular profiling for identification of microbial DNA and its utility as an additional tool for sepsis diagnostics [13, 54, 55].

We recognize that our study had limitations. Not all the blood samples analyzed had a sequencing depth that allowed for good interpretation of β-diversity [65]. In the blood samples the amount of bacterial DNA template was low as compared to the host template resulting in the high relative abundance of the “noRoot” OTU. This “noRoot” OTU was attributed to the well-documented erroneous amplification of human DNA in clinical samples with universal 16S rRNA gene primers. This issue has been reported since the early days of PCR [66] and is still problematic in contemporary 16S rRNA gene studies [6668]. In this study, the abundance of “noRoot” DNA often represented a large portion of the amplified sequences in whole blood. This was unique to our study and likely reflected the low ratio of bacterial to host DNA in these samples. It is difficult to know the exact concentration of bacteria in bloodstream infections since the blood culture results only indicate the CFU/ml of bacteria after a broth-enrichment. However, in the limited number of samples where culture from saponin treated whole blood was successful, the CFU/ml were between 1 to 30 (data not shown) suggesting the concentration of bacteria would be low in the clinical samples. Following the removal of the “noRoot” reads, the samples often had a low number of remaining sequences. A reasonable cut-off was needed to ensure that differences in the taxonomic structure of samples could be identified. The strength of UniFrac beta-diversity to identify meaningful patterns in various datasets has been well documented [27]. Even in small sample size simulations (50 sequences) the UniFrac values could be used to discriminate between samples [27]. However, when the expected similarity in microbial communities among different samples was anticipated to be high, more sequencing reads were required to identify relationships [27]. It is also known that between 500 and 1000 reads/sample is sufficient, but not ideal, to distinguish differences in phylogenetic composition between two samples using beta-diversity. As such, a depth of 500 reads was selected as it permitted evaluation of more of the samples with the knowledge that the interpretation of the profiles required caution in the absence of good clinical data. When compared to other molecular profiling studies of blood our abundance threshold was significantly lower [13]. Given the difference in DNA extraction protocols, PCR amplification, sequencing platforms and analysis methods it is difficult to compare the quality of sequencing data based on abundance per sample. Prior to extensive filtering the read per sample averaged at over 14,000 reads which is in line with other molecular profiling studies [13]. The lower abundance per sample was interpreted to reflect the low ratio of bacterial DNA sequences compared to the “noRoot” human DNA (Table 2). Since this was a ratio-based issue, the use of larger blood volumes was not predicted to circumvent these limitations. Nevertheless, the removal of these DNA sequences from the taxonomic profile enabled the analysis of the remaining, low proportion, bacterial DNA in the samples. Although this resulted in many samples not being fully analyzed, PCoA analysis indicated that low sequence depth samples still clustered alongside SB samples (Fig. 2). This would suggest that most whole blood samples had similar molecular profiles to the SB samples in Fig. 1 despite lower sequencing depth.

Another limitation was that the bacterial DNA profiles reflected relative not total DNA abundance. This meant that no conclusions the quantity of bacterial DNA in these samples. Attempts to quantitate the bacterial load in the HB and SB samples, using RT-PCR, were unsuccessful due to the cross-reactivity of the 16S primers to human DNA in these samples (data not shown).

As such, the bacterial DNA profiles could indicate the taxonomic diversity in each sample but not the bacterial load. Finally, we reiterate that the molecular analysis identifies the presence of bacterial DNA not viable organisms. The isolation protocol described previously [9] should reduce the level of free DNA in the preparation and therefore these profiles should be enriched in DNA from intact cells.

Based on these limitations, it was essential that each sample was evaluated within the clinical context. Many reviews of molecular profiling strategies have highlighted the importance of analyzing molecular data in conjunction with other clinical measures of severity (i.e., APACHE, SOFA scores), markers of infection (i.e., procalcitonin), and markers of inflammation (i.e., IL-6, IL-10) [6972]. However, our findings reveal that when the bacterial DNA patterns were aligned with the clinical data it was apparent that meaningful patterns were observed in the data. Despite this, our data also highlighted discordance between blood culture enrichment and molecular sequencing can occur. When results were discordant, there was often other clinical data to support the molecular sequencing (Fig. 1a, Table 3). In other cases, the discordance was thought to result from difference in the time of collection as well as reflecting discrepancy between a broth enrichment method to that of direct sampling [69, 7375]. While our initial blood samples were obtained within 24 h of ICU admission or in the ED once sepsis was suspected, it did not guarantee that our samples were collected prior to initiation of antimicrobial therapy. With early antibiotic therapy being a hallmark of sepsis management as outlined in sepsis guidelines, it was predicted that the majority of our ICU patient samples were obtained after antimicrobial therapy was started whereas blood culture results are often obtained prior to antimicrobial therapy [11, 76, 77]. As such, effective antimicrobial therapy was also considered when discordance was present.

Conclusions

The overall evaluation of a whole blood molecular profiling approach to evaluating septic bloodstream infections provided several novel findings. Overall, the bacterial DNA profiling of whole blood samples from adult and pediatric patients was correlated to a predicted bloodstream infection with either a viable organism or bacterial products in 75% of the samples analyzed in this study. In addition, the molecular profiling data predicted a greater role for polymicrobial infections in the pathogenesis of sepsis.

Abbreviations

APACHE: 

Acute Physiology and Chronic Health Evaluation

ASN: 

Alberta Sepsis Network

CFU: 

Colony forming unit

CoNS: 

Coagulase-negative Staphylococci

ED: 

Emergency department

ICU: 

Intensive care unit

MRSA: 

Methicillin-resistant Staphylococcus aureus

OTU: 

Operational taxonomic unit

PCoA: 

Principal coordinates analysis

PCR: 

Polymerase chain reaction

SIRS: 

Systemic inflammatory response syndrome

SOFA: 

Sepsis related organ failure assessment

UPGMA: 

Unweighted pair group method with arithmetic mean

Declarations

Acknowledgements

We thank the manager and co-ordinator of the CCEPTR resource, Joseé Wong, for her assistance with sample collection. We thank Dr. Dean Yergens for providing clinical data for this study. Special thanks to the research nurses and assistants who identified and enrolled patients with the ICU and ED at Foothills Medical Centre and the Alberta Children’s Hospital, Calgary, Canada.

Funding

This research was supported by a five year (2009–2014) Alberta Heritage Foundation for Medical Research (now known as Alberta Innovates) inter-disciplinary team grant. This grant supported the formation of the Alberta Sepsis Network. Project leads included: Drs. Chip Doig, Paul Kubes, and Ari Joffe. Drs. Michael Surette and John Conly were co-investigators. The Alberta Sepsis Network facilitated access to clinical samples and clinical data collection for this study. Design of the study and analysis of the data was done without any involvement from the Alberta Sepsis Network. Dr. Faria received stipend support for this research through the Alberta Sepsis Network grant funding. Alberta Innovates did not have a role in the design of the study, collection of samples, interpretation of the data, or in the preparation of this manuscript.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

MMPF carried out all the studies and drafted the manuscript. BW provided access to clinical samples through CCEPTR and contributed to revisions of this manuscript. MGS conceived of the study and performed the molecular sequencing analysis. JMC contributed to the coordination of the study and helped draft the manuscript. All authors read and approved of the final manuscript.

Ethics approval and consent to participate

Approval for this study was obtained from the Conjoint Health Research Ethics Board of the University of Calgary. All human samples were collected following the guidelines outlined in the Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, and Social Sciences and Humanities Research Council of Canada, Tri-Council Policy Statement for the “Ethical Conduct for Research Involving Humans” dated December 2010.

Human blood samples were collected as part of the Critical Care Epidemiologic and Biologic Tissue Resource (CCEPTR). Approval for CCEPTR was granted by the Conjoint Health Research Ethics Board of the University of Calgary with the Ethics ID for the study E-22236 on April 7, 2009. Informed written consent was obtained from all patients or their substitute decision maker prior to collecting samples. Substitute decision makers provided written consent when the patient was not able to provide consent due to altered level of consciousness or physical impairment. For patients considered as minors, under the age of 18, a legal guardian or parent provided written consent.

Consent for publication

Not applicable to this manuscript.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Department of Microbiology, Immunology and Infectious Diseases, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada
(2)
Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada
(3)
Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada
(4)
Department of Critical Care, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada
(5)
Calvin, Phoebe and Joan Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada
(6)
O’Brien Institute for Public Health, University of Calgary, Calgary, AB, T2N 4N1, Canada
(7)
Farncombe Family Digestive Health Research Institute, McMaster University, Hamilton, ON, L8S 4K1, Canada
(8)
Department of Medicine and Biochemistry, Faculty of Health Sciences, McMaster University, Hamilton, ON, L8S 4K1, Canada
(9)
Department of Biomedical Sciences, Faculty of Health Science, McMaster University, Hamilton, ON, L8S 4K1, Canada
(10)
Foothills Medical Centre, Alberta Health Services, Room AGW5, 1403 29th Street NW, Calgary, AB, T2N 2T9, Canada

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